The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Recently, with an increasing demand for improvement of fuel efficiency of vehicles and stringent regulations on emissions from vehicles in many countries, the demand for environmentally friendly vehicles has been increased. In order to meet this demand, hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) have been developed.
A hybrid vehicle runs using two power sources including an engine and a motor. Depending on harmonious operation of the engine and the motor, optimal output and torque may be generated. Specifically, in the case of a hybrid vehicle equipped with a parallel-type (or transmission-mounted-electric-device-type (TMED-type)) hybrid system, in which an electric motor and an engine clutch (EC) are installed between an engine and a transmission, the output of the engine and the output of the motor may be transmitted to a driving shaft simultaneously.
Compared to a vehicle equipped with general internal combustion engine, a hybrid vehicle has more factors to be determined and controlled in order to achieve efficient driving, for example, whether to perform engagement of an engine clutch, whether to apply a motor output to an engine output, and whether to change a driving mode. If a driver's behavior in the near future (e.g. within 10 seconds) can be predicted, more efficient driving may be realized.
In order to predict a driver's behavior, research on a method of sampling driver's driving patterns using a machine-learning method and of updating the samples through learning has been actively conducted. However, this method does not reflect respective drivers' peculiarities. That is, in the case in which a single prediction model is generated based on acquisition of driving data of a plurality of drivers and learning of the drivers' patterns, prediction accuracy may greatly vary depending on drivers' peculiarities. This will now be explained with reference to FIGS. 1 and 2.
FIGS. 1 and 2 are views for explaining respective problems with conventional learning of a driver's patterns.
First, referring to FIG. 1, it can be seen that a driver A decelerates in advance before the vehicle approaches a speed camera and a driver B decelerates suddenly when the vehicle closely approaches a speed camera. In the case in which learning of a driving pattern is conducted based on samples derived from these two drivers, the calculated prediction result merely corresponds to an intermediate value, which is a simple mean value of the sample values. Therefore, the prediction model generated through this method is not actually suitable for either of the two drivers.
This problem may also occur even when learning of a driving pattern is conducted based on samples derived from one driver, who decelerates suddenly when the vehicle has almost reached a tollgate but decelerates well in advance of a speed camera, as shown in FIG. 2.
Therefore, we have discovered that a conventional learning scheme, which does not reflect respective drivers' peculiar driving patterns with respect to the same or different upcoming events, may not accurately predict a driver's intention to accelerate or decelerate in the near future.